Browse by UCL people
Group by: Type | Date
Number of items: 77.
2016
Jitkrittum, W;
Szabo, Z;
Chwialkowski, K;
Gretton, A;
(2016)
Interpretable Distribution Features with Maximum Testing Power.
ArXiv
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Jitkrittum, W;
Szabo, Z;
Chwialkowski, K;
Gretton, A;
(2016)
Distinguishing distributions with interpretable features.
Presented at: International Conference on Machine Learning (ICML): Data-Efficient Machine Learning workshop, New York, USA.
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Jitkrittum, W;
Szabo, Z;
Chwialkowski, K;
Gretton, A;
(2016)
Distinguishing distributions with interpretable features.
Presented at: International Conference on Machine Learning (ICML): Data-Efficient Machine Learning workshop, New York, USA.
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Jitkrittum, W;
Szabo, Z;
Chwialkowski, K;
Gretton, A;
(2016)
Distinguishing distributions with interpretable features.
In:
ICML 2016 Workshop on Data-Efficient Machine Learning.
: New York, USA.
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Sriperumbudur, B;
Szabo, Z;
(2016)
Optimal Uniform and Lp Rates for Random Fourier Features.
Presented at: Theory of Big Data Workshop, London, United Kingdom.
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Strathmann, H;
Sejdinovic, D;
Livingston, S;
Schuster, I;
Lomeli Garcia, M;
Szabo, Z;
Andrieu, C;
(2016)
Kernel techniques for adaptive Monte Carlo methods.
Presented at: Greek Stochastics Workshop on Big Data and Big Models, Tinos, Greek.
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Szabo, Zolt´an;
Sriperumbudur, Bharath K;
Poczos, Barnab´as;
Gretton, Arthur;
(2016)
Learning Theory for Distribution Regression.
Journal of Machine Learning Research
, 17
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Szabo, Z;
(2016)
Hypothesis Testing with Kernels.
Presented at: International Workshop on Pattern Recognition in Neuroimaging (PRNI), Trento, Italy.
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Szabo, Z;
(2016)
Kernel-based learning on probability distributions.
Presented at: UNSPECIFIED, San Diego, California, USA.
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Szabo, Z;
(2016)
Performance guarantees for kernel-based learning on probability distributions.
Presented at: Talk at Special Symposium on Intelligent Systems, MPI Tübingen, Germany.
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Szabo, Z;
(2016)
Optimal Rates for the Random Fourier Feature Technique.
Presented at: invited talk at École Polytechnique, Palaiseau, France.
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Szabo, Z;
(2016)
Learning from Features of Sets and Probabilities.
Presented at: Talk at Imperial College London, Department of Computing, London, United Kingdom.
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Szabo, Z;
Sriperumbudur, B;
Poczos, B;
Gretton, A;
(2016)
Optimal Regression on Sets.
Presented at: eResearch Domain launch event, London, United Kingdom.
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Szabo, Z;
Sriperumbudur, B;
Poczos, B;
Gretton, A;
(2016)
Distribution Regression with Minimax-Optimal Guarantee.
Presented at: MASCOT-NUM 2016, Toulouse, France.
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2015
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Jitkrittum, W;
Gretton, A;
Heess, N;
Eslami, A;
Lakshminarayanan, B;
Sejdinovic, D;
Szabo, Z;
(2015)
Just-In-Time Kernel Regression for Expectation Propagation.
In:
Proceeding of Large-Scale Kernel Learning: Challenges and New Opportunities workshop.
: Lille, France.
(In press).
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Jitkrittum, W;
Gretton, A;
Heess, N;
Eslami, A;
Lakshminarayanan, B;
Sejdinovic, D;
Szabo, Z;
(2015)
Kernel-Based Just-In-Time Learning for Passing Expectation Propagation Messages.
Presented at: Data, Learning and Inference workshop (DALI), La Palma (Canaries, Spain).
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Jitkrittum, W;
Gretton, A;
Heess, N;
Eslami, SMA;
Lakshminarayanan, B;
Sejdinovic, D;
Szabó, Z;
(2015)
Kernel-based just-in-time learning for passing expectation propagation messages.
In: Meila, Marina and Heskes, Tom, (eds.)
Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence (UAI'15 ).
(pp. pp. 405-414).
AUAI Press: Virginia, USA.
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Park, M;
Jitkrittum, W;
Qamar, A;
Szabo, Z;
Buesing, L;
Sahani, M;
(2015)
Bayesian Manifold Learning: Locally Linear Latent Variable Model (LL-LVM).
Presented at: Quinquennial Review Symposium, London, United Kingdom.
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Sriperumbudur, B;
Szabo, Z;
(2015)
Optimal Uniform and Lp Rates for Random Fourier Features.
Presented at: Talk at Pennsylvania State University, Pennsylvania State University, USA.
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Sriperumbudur, B;
Szabo, Z;
(2015)
Optimal Rates for Random Fourier Feature Approximations.
Presented at: Talk at University of Alberta, Edmonton, Alberta, Canada.
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Sriperumbudur, B;
Szabo, Z;
(2015)
Optimal Rates for Random Fourier Feature Kernel Approximations.
Presented at: Talk at UC Berkeley: AMPLab, Berkeley, California.
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Sriperumbudur, B;
Szabo, Z;
(2015)
Optimal Uniform and Lp Rates for Random Fourier Features.
Presented at: Quinquennial Review Symposium, Gatsby Unit, London, Unite Kingdom.
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Sriperumbudur, B;
Szabo, Z;
(2015)
Optimal Rates for Random Fourier Features.
Presented at: Neural Information Processing Systems (NIPS-2015), Montréal, Canada.
(In press).
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Sriperumbudur, B;
Szabo, Z;
(2015)
Performance Guarantees for Random Fourier Features - Limitations and Merits.
Presented at: ML@SITraN, University of Sheffield, Sheffield, Unite Kingdom.
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Strathmann, H;
Sejdinovic, D;
Livingstone, S;
Szabo, Z;
Gretton, A;
(2015)
Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families.
In: Cortes, C and Lawrence, ND and Lee, DD and Sugiyama, M and Garnett, R, (eds.)
Advances in Neural Information Processing Systems 28 (NIPS 2015).
NIPS Proceedings
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Szabó, Z;
Sriperumbudur, BK;
(2015)
Optimal Rates for the Random Fourier Feature Method.
Presented at: Talk at Carnegie Mellon University: Statistical ML Reading Group, Pittsburgh, PA, USA.
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Szabo, Z;
Gretton, A;
Póczos, B;
Sriperumbudur, B;
(2015)
Consistent Vector-valued Distribution Regression.
Presented at: UCL Workshop on the Theory of Big Data, London, UK.
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Szabo, Z;
Gretton, A;
Poczos, B;
Sriperumbudur, B;
(2015)
Two-stage Sampled Learning Theory on Distributions.
In: Lebanon, G and Vishwanathan, SVN, (eds.)
Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics.
(pp. pp. 948-957).
Journal of Machine Learning Research: San Diego, CA, USA.
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Szabo, Z;
Sriperumbudur, B;
Poczos, B;
Gretton, A;
(2015)
Learning Theory for Vector-Valued Distribution Regression.
Presented at: CMStatistics 2015, London, United Kingdom.
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Szabo, Z;
Sriperumbudur, B;
Poczos, B;
Gretton, A;
(2015)
Distribution Regression: Computational and Statistical Tradeoffs.
Presented at: CSML Lunch Talk Series, London, United Kingdom.
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Szabo, Z;
Sriperumbudur, B;
Poczos, B;
Gretton, A;
(2015)
Distribution Regression: Computational and Statistical Tradeoffs.
Presented at: Talk at Princeton University, Princeton, New Jersey.
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Szabo, Z;
Sriperumbudur, B;
Poczos, B;
Gretton, A;
(2015)
Regression on Probability Measures: A Simple and Consistent Algorithm.
Presented at: CRiSM Seminars, Department of Statistics, University of Warwick, Coventry, United Kingdom.
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Szabo, Z;
Sriperumbudur, B;
Poczos, B;
Gretton, A;
(2015)
Vector-valued Distribution Regression - Keep It Simple and Consistent.
Presented at: CSML reading group, Department of Statistics, University of Oxford, Oxford, United Kingdom.
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Szabo, Z;
Sriperumbudur, B;
Poczos, B;
Gretton, A;
(2015)
Distribution Regression - Make It Simple and Consistent.
Presented at: Data, Learning and Inference workshop (DALI), La Palma (Canaries, Spain).
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Szabo, Z;
Sriperumbudur, B;
Poczos, B;
Gretton, A;
(2015)
A Simple and Consistent Technique for Vector-valued Distribution Regression.
Presented at: Invited talk at the Artificial Intelligence and Natural Computation seminars, University of Birmingham, UK.
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Szabo, Z;
Sriperumbudur, B;
Poczos, B;
Gretton, A;
(2015)
Consistent Vector-valued Regression on Probability Measures.
Presented at: Invited talk at Prof. Bernhard Schölkopf's lab, Tübingen.
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2014
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Jeni, L;
Lőrincz, A;
Szabo, Z;
Cohn, J;
Kanade, T;
(2014)
Spatio-temporal event classification using time-series kernel based structured sparsity.
In: Fleet, D and Pajdla, T and Schiele, B and Tuytelaars, T, (eds.)
Computer Vision – ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part IV.
(pp. pp. 135-150).
Springer International Publishing: Switzerland.
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Park, M;
Jitkrittum, W;
Qamar, A;
Szabo, Z;
Buesing, L;
Sahani, M;
(2014)
Bayesian Manifold Learning: The Locally Linear Latent Variable Model (LL-LVM).
In:
Advances in Neural Information Processing Systems 28 (NIPS 2015).
Neural Information Processing Systems Foundation: Montreal, Canada.
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Szabo, Z;
(2014)
Information Theoretical Estimators Toolbox.
Journal of Machine Learning Research
, 15
283 - 287.
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Szabo, Z;
Gretton, A;
Poczos, B;
Sriperumbudur, B;
(2014)
Vector-valued distribution regression: a simple and consistent approach.
Presented at: Statistical Science Seminars, London, UK.
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Szabo, Z;
Gretton, A;
Póczos, B;
Sriperumbudur, B;
(2014)
Simple consistent distribution regression on compact metric domains.
Presented at: UCL-Duke Workshop on Sensing and Analysis of High-Dimensional Data (SAHD-2014), London, UK.
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Szabo, Z;
Gretton, A;
Póczos, B;
Sriperumbudur, B;
(2014)
Distribution Regression - the Set Kernel Heuristic is Consistent.
Presented at: CSML Lunch Talk Series, London, UK.
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Szabo, Z;
Gretton, A;
Póczos, B;
Sriperumbudur, B;
(2014)
Learning on Distributions.
Presented at: Kernel methods for big data workshop, Lille, France.
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Szabo, Z;
Gretton, A;
Póczos, B;
Sriperumbudur, B;
(2014)
Consistent Distribution Regression via Mean Embedding.
Presented at: University of Hertfordshire, Computer Science Research Colloquium, Hatfield, UK.
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2013
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Lőrincz, A;
Jeni, L;
Szabo, Z;
Cohn, J;
Kanade, T;
(2013)
Emotional expression classification using time-series kernels.
In:
2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
(pp. 889 - 895).
IEEE
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Pintér, B;
Vörös, G;
Palotai, Z;
Szabo, Z;
Lőrincz, A;
(2013)
Determining Unintelligible Words from their Textual Contexts.
Procedia - Social and Behavioral Sciences
, 73
101 - 108.
10.1016/j.sbspro.2013.02.028.
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Pintér, B;
Vörös, G;
Szabo, Z;
Lőrincz, A;
(2013)
Explaining unintelligible words by means of their context.
In:
(Proceedings) International Conference on Pattern Recognition Applications and Methods (ICPRAM).
(pp. 382 - 387).
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Szabo, Z;
(2013)
Information Theoretical Estimators (ITE) Toolbox.
Presented at: Neural Information Processing Systems (NIPS) - Workshop on Machine Learning Open Source Software, Harrahs and Harveys, Lake Tahoe, Nevada, United States.
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2012
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Jeni, LA;
Lőrincz, A;
Nagy, T;
Palotai, Z;
Sebők, J;
Szabó, Z;
Takács, D;
(2012)
3D shape estimation in video sequences provides high precision evaluation of facial expressions.
Image and Vision Computing
, 30
(10)
785 - 795.
10.1016/j.imavis.2012.02.003.
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Pintér, B;
Vörös, G;
Szabo, Z;
Lőrincz, A;
(2012)
Automated Word Puzzle Generation via Topic Dictionaries.
Presented at: International Conference on Machine Learning (ICML) - Sparsity, Dictionaries and Projections in Machine Learning and Signal Processing Workshop, Edinburgh, Scotland.
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Pintér, B;
Vörös, G;
Szabo, Z;
Lőrincz, A;
(2012)
Automated Word Puzzle Generation Using Topic Models and Semantic Relatedness Measures.
Presented at: Joint Conference on Mathematics and Computer Science (MACS), Siófok, Hungary.
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Pintér, B;
Vörös, G;
Szabo, Z;
Lőrincz, A;
(2012)
Automated Word Puzzle Generation Using Topic Models and Semantic Relatedness Measures.
Annales Universitatis Scientiarum Budapestinensis de Rolando Eötvös Nominatae, Sectio Computatorica
, 36
299 - 322.
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Szabo, Z;
(2012)
Group-Structured and Independent Subspace Based Dictionary Learning.
Doctoral thesis , Eötvös Loránd University.
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Szabo, Z;
Póczos, A;
Lőrincz, A;
(2012)
Collaborative Filtering via Group-Structured Dictionary Learning.
In:
(Proceedings) International Conference on Latent Variable Analysis and Signal Separation (LVA/ICA).
(pp. 247 - 254).
Springer-Verlag, Berlin Heidelberg
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Szabo, Z;
Póczos, B;
Lőrincz, A;
(2012)
Separation theorem for independent subspace analysis and its consequences.
Pattern Recognition
, 45
(4)
1782 - 1791.
10.1016/j.patcog.2011.09.007.
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2011
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Póczos, B;
Szabo, Z;
Schneider, J;
(2011)
Nonparametric divergence estimators for Independent Subspace Analysis.
In:
(Proceedings) European Signal Processing Conference (EUSIPCO) - Special Session on Dependent Component Analysis.
(pp. 1849 - 1853).
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Szabo, Z;
Póczos, B;
(2011)
Nonparametric Independent Process Analysis.
Presented at: European Signal Processing Conference (EUSIPCO), Barcelona, Spain.
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Szabo, Z;
Póczos, B;
Lőrincz, A;
(2011)
Online Dictionary Learning with Group Structure Inducing Norms.
Presented at: International Conference on Machine Learning (ICML) - Structured Sparsity: Learning and Inference Workshop, Bellevue, Washington, USA.
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Szabo, Z;
Póczos, B;
Lőrincz, A;
(2011)
Online Group-Structured Dictionary Learning.
In:
2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
(pp. 2865 - 2872).
IEEE
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2010
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Szabo, Z;
(2010)
Auto-Regressive Independent Process Analysis without Combinatorial Efforts.
Pattern Analysis and Applications
, 13
1 - 13.
10.1007/s10044-009-0174-x.
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Szabo, Z;
(2010)
Autoregressive Independent Process Analysis with Missing Observations.
In:
Proceedings of ESANN 2010: 18th European Symposium on Artificial Neural Networks.
(pp. 159 - 164).
D-Side Publications
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2009
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Szabo, Z;
(2009)
Complete Blind Subspace Deconvolution.
In: Adali, T and Jutten, C and Romano, JMT and Barros, AK, (eds.)
Independent Component Analysis and Signal Separation: 8th International Conference, ICA 2009, Paraty, Brazil, March 15-18, 2009. Proceedings.
(138 - 145).
Springer-Verlag Berlin Heidelberg
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Szabo, Z;
(2009)
Separation Principles in Independent Process Analysis.
Doctoral thesis , Eötvös Loránd University, Budapest.
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Szabo, Z;
Lőrincz, A;
(2009)
Complex Independent Process Analysis.
Acta Cybernetica
, 19
177 - 190.
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Szabo, Z;
Lőrincz, A;
(2009)
Controlled Complete ARMA Independent Process Analysis.
In:
IJCNN 2009. International Joint Conference on Neural Networks, 2009.
(3038 - 3045).
IEEE
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Szabo, Z;
Lőrincz, A;
(2009)
Fast Parallel Estimation of High Dimensional Information Theoretical Quantities with Low Dimensional Random Projection Ensembles.
In: Adali, T and Jutten, C and Romano, JMT and Barros, AK, (eds.)
Independent Component Analysis and Signal Separation: 8th International Conference, ICA 2009, Paraty, Brazil, March 15-18, 2009. Proceedings.
(146 - 153).
Springer-Verlag Berlin Heidelberg
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2008
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Szabo, Z;
Lőrincz, A;
(2008)
Towards Independent Subspace Analysis in Controlled Dynamical Systems.
Presented at: ICA Research Network International Workshop (ICARN), Liverpool, U.K..
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2007
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Lőrincz, A;
Szabo, Z;
(2007)
Neurally Plausible, Non-combinatorial Iterative Independent Process Analysis.
Neurocomputing - Letters
, 70
(7-9)
1569 - 1573.
10.1016/j.neucom.2006.10.145.
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Póczos, B;
Szabo, Z;
Kiszlinger, M;
Lőrincz, A;
(2007)
Independent Process Analysis without A Priori Dimensional Information.
In: Davies, ME and James, CJ and Abdallah, SA and Plumbley, MD, (eds.)
Independent Component Analysis and Signal Separation: Proceedings of the 7th International Conference, ICA 2007, London, UK, September 9-12, 2007.
(pp. 252 - 259).
Springer-Verlag Heidelberg
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Szabo, Z;
Lőrincz, A;
(2007)
Multilayer Kerceptron.
Journal of Applied Mathematics
, 24
209 - 222.
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Szabo, Z;
Póczos, B;
Lőrincz, A;
(2007)
Undercomplete Blind Subspace Deconvolution via Linear Prediction.
In: Kok, JN and Koronacki, J and Mantaras, RL and Matwin, S and Mladenic, D and Skowron, A, (eds.)
Machine Learning: ECML 2007. Proceedings of the 18th European Conference on Machine Learning, Warsaw, Poland, September 17-21, 2007.
(pp. 740 - 747).
Springer-Verlag, Berlin Heidelberg
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Szabo, Z;
Póczos, B;
Szirtes, G;
Lőrincz, A;
(2007)
Post Nonlinear Independent Subspace Analysis.
In: Sa, JM and Alexandre, LA and Duch, W and Mandic, DP, (eds.)
Artificial Neural Networks – ICANN 2007: Proceedings of the 17th International Conference, Porto, Portugal, September 9-13, 2007, Part I.
(pp. 677 - 686).
Springer-Verlag, Berlin Heidelberg
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2006
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Szabo, Z;
Lorincz, A;
(2006)
Real and Complex Independent Subspace Analysis by Generalized Variance.
In:
ICA Research Network International Workshop (ICARN).
(pp. 85 - 88).
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Szabo, Z;
Póczos, B;
Lőrincz, A;
(2006)
Cross-Entropy Optimization for Independent Process Analysis.
In: Rosca, J and Erdogmus, D and Príncipe, J and Haykin, S, (eds.)
Independent Component Analysis and Blind Signal Separation: . Proceedings of the 6th International Conference, ICA 2006, Charleston, SC, USA, March 5-8, 2006.
(pp. 909 - 916).
Springer
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2005
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Szabo, Z;
Póczos, B;
Lőrincz, A;
(2005)
Separation Theorem for Independent Subspace Analysis.
: Eötvös Loránd University, Budapest.
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2004
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Hévízi, G;
Biczó, M;
Póczos, B;
Szabo, Z;
Takács, B;
Lőrincz, A;
(2004)
Hidden Markov model finds behavioral patterns of users working with a headmouse driven writing tool.
In:
Proceedings of 2004 IEEE International Joint Conference on Neural Networks, 2004.
(pp. 669 - 674).
IEEE
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2003
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Szabo, Z;
(2003)
Retina based sampling in face component recognition.
Masters thesis , Eötvös Loránd University, Budapest.
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